Soil Distribution Coefficients Research Articles

Perfluorooctanoic acid dominates the molecular-level effects of a mixture of equal masses of perfluorooctanoic acid and perfluorooctane sulfonic acid in earthworm

Per- and polyfluoroalkyl substances (PFAS) are an important class of emerging contaminants in the environment. Most studies on the impact of PFAS mixtures considered phenotypic endpoints, which may not adequately reflect the sublethal effects on organisms. To fill this knowledge gap, we investigated the subchronic impact of environmentally relevant concentrations of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS)—as individual compounds and a mixture (PFOS+PFOA)—on earthworm (Eisenia fetida), using phenotypic and molecular endpoints. PFAS decreased the survival (12.2–16.3%), biomass (9.0–9.8%), and reproduction (15.6–19.8%) of E. fetida after 28 d of exposure. The bioaccumulation of PFOS after 28 d increased (from 2790.7 ng/g-dw to 5224.9 ng/g-dw) while that of PFOA decreased (from 780.2 ng/g-dw to 280.5 ng/g-dw) when E. fetida was exposed to the mixture compared to the individual compounds. These bioaccumulation trends were partly attributed to changes in the soil distribution coefficient (Kd) of PFOS and PFOA when present in the mixture. Eighty percent of the (p and FDR < 0.05) altered metabolites after 28 d were similarly perturbed by both PFOA and PFOS+PFOA. The pathways dysregulated are related to the metabolism of amino acids, energy, and sulfur. We showed that PFOA dominates the molecular-level impact of the binary PFAS mixture.

Deriving probabilistic soil distribution coefficients (Kd). Part 2: Reducing caesium Kd uncertainty by accounting for experimental approach and soil properties

The solid-liquid distribution coefficient (Kd) is a key input parameter in radioecological models. However, its large variability hampers its usefulness in modelling transport processes as well as its accuracy in representing soil-radionuclide interactions. For the specific case of radiocaesium, the analyses of a Cs Kd soil dataset (769 entries) showed that values varied over a five order of magnitude range, and the resulting Cs Kd best estimate (calculated as a geometric mean = 2.5 × 103 L kg−1) lacked reliability and representativity. Grouping data and creation of partial datasets based on the experimental approach (short-term (< ~1 yr) vs. long-term experiments (> ~1 yr)) and soil factors affecting Cs interaction (i.e., the ratio of the radiocaesium interception potential (RIP) to the potassium content in soil solution (Kss); organic matter content (OM) and soil texture) succeeded in reducing variability a few orders of magnitude, with Cs Kd best estimates also differing by one-two orders of magnitude depending on the type of soil and experimental approach. The statistical comparison of the Cs Kd best estimates and related cumulative distribution functions of the partial datasets revealed a relevant effect of the sorption dynamics on Cs Kd values (with long-term values systematically higher than short-term ones), and that the RIP/Kss ratio was an excellent predictor of Cs Kd for short-term scenarios, whereas the RIP parameter could be predicted on the basis of texture information. The OM threshold to distinguish between OM threshold to distinguish between Mineral and Organic soils subclasses, regarding Cs interaction was determined to be 50% and 90% OM for short- and long-term scenarios, respectively. It was then recommended to select the Cs Kd input data depending on the soils and scenarios to be assessed (e.g., short- vs. long-term; OM %) to improve the reliability and decrease the uncertainty of the radioecological models.

Deriving probabilistic soil distribution coefficients (Kd). Part 3: Reducing variability of americium Kd best estimates using soil properties and chemical and geological material analogues

The solid-liquid distribution coefficient (Kd) is a key input parameter in radioecological risk models. However, its large variability hampers its usefulness in modelling transport processes as well as its accuracy in representing soil-radionuclide interactions. To assist in the selection of Kd values and their cumulative distribution functions for study areas without site specific information, a critically reviewed dataset was developed, containing more than 5000 soil Kd entries for 83 elements and an additional 2000 entries of Kd data for 75 elements gathered from a selection of other geological materials. For the specific case of americium (Am), the dataset contained 109 entries for soils and 33 additional entries for sediment and subsoils. The analysis of the Am Kd soil dataset showed that values varied 4-orders of magnitude, and consequently the resulting Am Kd best estimate (geometric mean (GM): 4.6 × 103 L kg−1) lacked sufficient reliability. The objective of this study was to calculate cumulative distribution functions and statistically evaluate this dataset to determine if the Am Kd variability in soils could be reduced by considering various factors, including: 1) measurement methods, 2) key soil properties, 3) the use of chemical analogue data, and 4) the use of analogue data. Accounting for Am Kd experimental method (i.e., sorption vs. desorption; long-vs. short-term experiments) had little effect on reducing variability. However, accounting for key soil factors (i.e., organic matter content (OM), pH, soil texture) succeeded in reducing variability of Am Kd, especially when combining pH and OM. While previous data sets have used 20% OM content as a critical value to distinguish between mineral and organic soils, this study shows that this critical value should be reduced to 10% OM to minimize Am Kd variability. The inclusion in the dataset of Am Kd from other geological materials (e.g., gyttjas, tills, and subsoils) and Kd values from trivalent lanthanides (Ln (III)) and actinides (An (III)) (172 additional entries) did not statistically affect the Am Kd geometric means of the various pH and OM partial datasets. The larger composite dataset (> 310 entries), with both chemical analogues and geological material analogues to address data gaps, increased the statistical power for calculating Kd best estimates with lower variability, thereby enhancing their usefulness for radionuclide risk calculations.

Deriving probabilistic soil distribution coefficients (Kd). Part 1: General approach to decreasing and describing variability and example using uranium Kd values

A general approach is presented to derive probabilistic radionuclide distribution coefficients (Kd) in soils from a Kd dataset. The main aim was to derive informed estimates with a low inherent uncertainty by restricting the Kd value data to subsets based on key soil factors and the experimental approach used to calculate the Kd value (e.g., sorption and desorption tests). As an example, the general approach was applied to uranium (U) Kd values that are part of a critically reviewed dataset containing more than 5000 soil Kd entries for 83 elements and an additional 2000 entries of Kd data for 75 elements gathered from a selection of other, non-soil, geological materials. The overall soil U Kd dataset included 196 values spanning a range of four orders of magnitude (1–67,000 L kg−1), with additional 50 entries for other geological materials. Whereas the effect of the experimental approach could be disregarded, major factors in decreasing U Kd variability were pH and organic matter content (OM). Limitation in the number of entries made it difficult to use texture information (sand, silt, clay) to further decrease U Kd variability. The integrated combination of pH + OM permitted some soil groups to have U Kd confidence intervals as narrow as two orders of magnitude. Specifically for U Kd, data in the Mineral (< 20% OM) and Organic (≥ 20% OM) partial datasets were significantly different. Analogue data from geological materials other than soils, such as subsoil, till and gyttja (a lacustrine mud having elevated organic matter (OM) contents), were also statistically evaluated to determine whether they could be used to fill U Kd data gaps. It was shown that U Kd from subsoils and tills, but not gyttjas, could be used to enhance soil U Kd datasets. Selection of probabilistic Kd values for risk modelling can be made more reliably and with less uncertainty by using appropriate geochemical data representative of the study site to narrow the wide range of potential Kd values.

Estimation of distribution coefficient of uranium in soil by batch tests

Sorption studies were performed to estimate the soil distribution coefficient (Kd). Since soil and groundwater are major controlling factors for the Kd value, it is important to determine the site specific Kd value. In this study, special emphasis has been given to soil and groundwater parameters which control the Kd value of uranium around the mineralized zone. Based on the analysis of soil sample, the distribution coefficient of uranium around the mineralized zone varies from 7 to 369 l/kg. It is also observed that distribution coefficient of uranium in this region not only depends on the individual parameter and it varies significantly with space. Increase and decrease in Kd value was observed when the pH ranges from 4 – 7 and 7 – 8.5 respectively which indicates that the increase in pH greater than 7 will reduce the Kd value. No significant correlation was observed between the Kd value and other parameters such as soil texture, CaCO3, CEC and major ion concentration in groundwater. Determination of Kd value in a uranium mineralized zone will be used to predict and model the contaminant transport.

Removal and attenuation of sewage effluent combined tracer signals of phosphorus, caffeine and saccharin in soil

Contaminants in septic tank effluent (STE) are expected to be removed by the soil system before discharging to the environment. However, potential contaminants such as phosphorus (P), caffeine and artificial sweeteners do find their way to watercourses impacting aquatic eco systems. In this study, the attenuation of STE P, caffeine and saccharin were investigated in untreated soil and in soil with reduced microbial activity, in aqueous solutions and in the complex matrix of STE. Time series sorption and desorption experiments using batch equilibrium and a column experiment of STE P attenuation were conducted. The results revealed that the soil distribution coefficients (Kd) were: P 81.57 > caffeine 22.16 > saccharin 5.98 cm3/g, suggesting greater soil affinity to P adsorption. The data revealed that 80% of saccharin and 33% of caffeine attenuation was associated with microbial activities rather than adsorption processes. However, a complete removal of saccharin and caffeine did not occur during the equilibration period, suggesting their leaching potential. The dominant mechanism of P attenuation was adsorption (chemical and physical), yielding P retention of >73% and 35% for P in aqueous solution and in STE matrix, respectively, for batch equilibrium. The soil in the column acted as effluent P sink retaining 125 μg P/g soil of effluent P. The attenuation of P, caffeine and saccharin in the aqueous solution was greater than in STE, suggesting that the complex composition of STE reduced soil adsorption ability, and that other substances present in STE may be competing for soil binding sites. The data revealed that caffeine and P had similarities in the interaction with soils and thus caffeine may be considered as a STE tracer of anthropogenic source of P in receiving waters.

Herbicide monitoring in soil, runoff waters and sediments in an olive orchard

Occurrences of surface water contamination by herbicides in areas where olive orchards are established reveal a need to understand soil processes affecting herbicide fate at field scale for this popular Mediterranean crop. A monitoring study with two herbicides (terbuthylazine and oxyfluorfen) in the first 2cm of soil, runoff waters, and sediments, was carried out after under natural rainfall conditions following winter herbicide application. At the end of the 107day field experiment, no residues of the soil applied terbuthylazine were recovered, whereas 42% of the oxyfluorfen applied remained in the top soil. Very low levels of both herbicides were measured in runoff waters; however, concentrations were slightly higher for terbuthylazine (0.53% of applied) than for oxyfluorfen (0.03% of applied), relating to their respective water solubilities. Congruent with soil residue data, 38.15% of the applied oxyfluorfen was found in runoff-sediment, compared to only 0.46% for terbuthylazine. Accordingly, the herbicide soil distribution coefficients measured within runoff field tanks was much greater for oxyfluorfen (Kd=3098) than for terbuthylazine (Kd=1.57). The herbicide oxyfluorfen is co-transported with sediment in runoff, remaining trapped and/or adsorbed to soil particle aggregates, due in part to its low water solubility. In contrast, terbuthylazine soil dissipation may be associated more so with leaching processes, favored by its high water solubility, low sorption, and slow degradation. By comparing these two herbicides, our results reaffirm the importance of herbicide physico-chemical properties in dictating their behavior in soil and also suggest that herbicides with low solubility, as seen in the case oxyfluorfen, remain susceptible to offsite transport associated with sediments.

Artificial neural network application for predicting soil distribution coefficient of nickel

The distribution (or partition) coefficient (Kd) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict Kd values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting Kd of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The Kd values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and Kd of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of Kd. Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network.

Comparison of mechanistic and PLS-based regression models to predict radiocaesium distribution coefficients in soils

Radiocaesium sorption interaction descriptors were examined in 30 soil samples from Spain. Mechanistic and regression models were used to predict the solid–liquid distribution coefficients of radiocaesium (Kd(Cs)) based on soil properties, and the obtained values were compared with the experimental ones, which were derived from batch experiments. The batch experiments used two contact solutions: one simulated the composition of the soil solution, and the other was the wash-off from the soil. Several mechanistic models of different complexity were tested based on the Radiocaesium Interception Potential (RIP), with satisfactory agreement between experimental and predicted values. A simplified model based on either the RIP, or the clay content and K status of the soil was proposed. Various multivariant regression models, which were constructed using the Partial Least Square Regression (PLS), were also evaluated. The RIP, clay content, and the K and NH4+ contents were also identified by the regression models as the most relevant soil parameters to predict the Kd. As seen for the mechanistic models, the goodness of fit of the regression models was demonstrated by an excellent agreement between experimental and predicted values.

The use of hard- and soft-modelling to predict radiostrontium solid–liquid distribution coefficients in soils

The solid–liquid distribution coefficient ( K d ) is the parameter that governs the incorporation of contaminants in soils. Its estimation allows the prediction of the fate of contaminants in the short- and long-term after a contamination event. Here, the K d of radiostrontium ( K d (Sr)), a radionuclide of significant environmental interest, was predicted by hard models, which are based on knowledge of the mechanisms governing its sorption, and by soft models based on Partial Least Squares (PLS), using a large data set with the main soil parameters. The two approaches were tested and compared for 30 soils in Spain. Correlations between the predicted and experimental values of K d (Sr) obtained using hard- and soft-modelling showed slopes close to 1 and regression coefficients higher than 0.95, which confirms that both approaches are able to obtain satisfactory estimates for K d (Sr) from soil parameters.

Bioconcentration factor estimates of polycyclic aromatic hydrocarbons in grains of corn plants cultivated in soils treated with sewage sludge

This study presents a model to simulate the organic substance concentrations in corn grains assuming that the substances in soil solution are absorbed via the transpiration stream by plants growing in soils fertilized with sewage sludge (SS). The model was applied and validated using soil and corn grain samples from a long-term field experiment with six successive yearly applications of SS to the soil. The following polycyclic aromatic hydrocarbons (PAHs) were simulated and evaluated in soil and grain samples: acenaphthene, acenaphthylene, anthracene, benz(a)anthracene, benz(a)pyrene, benz(b)fluoranthene, benz(g,h,i)perylene, benz(k)fluoranthene, chrysene, dibenz(a,h)anthracene, fluoranthene, fluorene, indeno(1,2,3-c,d)pyrene, naphthalene, phenanthrene and pyrene. The PAH bioconcentration factors ( BCF) in corn grains ranged from 1.57 to 10.97 L kg − 1 . Polycyclic aromatic hydrocarbons with low soil distribution coefficients and high values of transpiration stream concentration factors ( TSCF) are more likely to be absorbed by corn plants and accumulated in grains. It was possible to estimate and observe that highly lipophilic PAH molecules (heavy PAHs) show lower accumulative potential in corn grains than the less lipophilic ones (light PAHs). Sewage sludges containing significant concentrations of light PAHs with two, three or four benzene rings should be avoided as fertilizers in alimentary field crops.

New best estimates for radionuclide solid–liquid distribution coefficients in soils. Part 2. Naturally occurring radionuclides

Predicting the transfer of radionuclides in the environment for normal release, accidental, disposal or remediation scenarios in order to assess exposure requires the availability of an important number of generic parameter values. One of the key parameters in environmental assessment is the solid liquid distribution coefficient, K d, which is used to predict radionuclide–soil interaction and subsequent radionuclide transport in the soil column. This article presents a review of K d values for uranium, radium, lead, polonium and thorium based on an extensive literature survey, including recent publications. The K d estimates were presented per soil groups defined by their texture and organic matter content (Sand, Loam, Clay and Organic), although the texture class seemed not to significantly affect K d. Where relevant, other K d classification systems are proposed and correlations with soil parameters are highlighted. The K d values obtained in this compilation are compared with earlier review data.

New best estimates for radionuclide solid–liquid distribution coefficients in soils. Part 3: miscellany of radionuclides (Cd, Co, Ni, Zn, I, Se, Sb, Pu, Am, and others)

New best estimates for the solid–liquid distribution coefficient ( K d ) for a set of radionuclides are proposed, based on a selective data search and subsequent calculation of geometric means. The K d best estimates are calculated for soils grouped according to the texture and organic matter content. For a limited number of radionuclides this is extended to consider soil cofactors affecting soil–radionuclide interaction, such as pH, organic matter content, and radionuclide chemical speciation. Correlations between main soil properties and radionuclide K d are examined to complete the information derived from the best estimates with a rough prediction of K d based on soil parameters. Although there are still gaps for many radionuclides, new data from recent studies improve the calculation of K d best estimates for a number of radionuclides, such as selenium, antimony, and iodine.

NTP‐CERHR expert panel report on the reproductive and developmental toxicity of bisphenol A

Robert E. Chapin, Jane Adams, Kim Boekelheide, L. Earl Gray Jr, Simon W. Hayward, Peter S.J. Lees, Barry S. McIntyre, Kenneth M. Portier, Teresa M. Schnorr, Sherry G. Selevan, John G. Vandenbergh, and Susan R. Woskie Pfizer, Inc., Groton, CT University of Massachusetts, Boston, MA Brown University, Providence, RI U.S. Environmental Protection Agency, Research Triangle Park, NC Vanderbilt University Medical Center, Nashville, TN Johns Hopkins University, Baltimore, MD Schering Plough Research Institute, Summit, NJ American Cancer Society, Atlanta, GA National Institute for Occupational Safety and Health, Cincinnati, OH U.S. Public Health Service (Ret), Silver Spring, MD North Carolina State University, Raleigh, NC University of Massachusetts, Lowell, MA

Study of the behaviour of natural radionuclides in a Mediterranean actual system

There is a lack of information for Mediterranean climate conditions and ecosystems from the point of view of characterization of real data, to obtain parameter values to be applied in the modeling of the behavior of natural and artificial radionuclides following hypothetical releases from a radioactive waste repository. So there was designed a project dealing with the study and determination of physical, chemical and radiological parameters from local and regional data obtained in an area of the southwest of Spain where and old uranium mine was being restored. The project, running during two years, has been financed by the National Company for Radioactive Waste Management in Spain (ENRESA) and has been coordinated by the CIEMAT Research Center with the collaboration of the Department of Physics of the Extremadura University. Results of the project have been the characterization of the biosphere system considering meteorological, geological, and hydrological data. Chemical and physical characterization of local soils and crops associated, surface waters and sediments samples, have been considered. Radiological measurements of U, Th and Ra contents in soils, vegetables, sediments and waters have been taken. Range of several parameter values obtained have shown good agreement with literature data published, this will permit the application of these values to the modeling issues of biosphere in the performance assessment of radioactive waste repositories under Mediterranean climate conditions, as well as for a forecasting of similar conditions that will occur in the future in other temperate areas. 1. OBJECTIVES Main objectives of the investigation have been to characterize the area, considering climatic and meteorological conditions, geological features, hydrogeology, soils development, ecological conditions and anthropological features. These are considered under Biosphere System Description. Determination of parameter values for the transfer of uranium isotopes in the biospheric area has been performed. The methodology followed to select isotopes, sample types and sample points and materials and methods used to obtain the data for determining parameter values is described afterwards. The last part of the paper reports on examples of experimental and empirical values obtained for solid-liquid distribution coefficients in soils and sediments of the biospheric area under study.

Sorption of pesticides to novel materials: snail pedal mucus and blackfly silk

The distribution coefficients for 10 pesticides (atrazine, cyanazine, desmetryn, fenitrothion, malathion, parathion, prometryn, propazine, simazine, terbutryn) to both snail pedal mucus and blackfly larval silk are measured and found to be orders of magnitude higher than for soil organic carbon. The lowest values are found for the triazine herbicides (e.g. 19,000 ml g −1 for simazine on silk) and the highest for the organophosphorus insecticides (e.g. 242,000 ml g −1 for malathion). The distribution coefficients for pedal mucus are more difficult to quantify in terms of the organic carbon distribution coefficients but, by mass, are much higher than the corresponding soil distribution coefficients. The sorption affinities obtained for blackfly silk are similar in magnitude to literature data for sorption of pesticides and polyaromatic hydrocarbons to natural organic colloids.

The Distribution Coefficient of Atrazine with Illinois Soils: A Laboratory Exercise in Environmental Chemistry

The soil distribution coefficient (Kd) of the corn herbicide atrazine and the fraction of organic carbon (foc) were measured on Illinois soils of low and high organic-matter content collected by students. Reverse-phase high-performance liquid chromatography was used for the Kd determination, and the foc was measured through loss on ignition.Taking into account that uncertainty existed in both organic-matter fraction and distribution coefficient data, least squares analysis showed that the distribution coefficient varied linearly with the foc and followed the relationship Kd = 57.2 (± 4.9) x foc -0.06 (± 0.04). This confirms that soils with high levels of organic matter will retain organic molecules of low water solubility better than soils with low levels of organic matter.Students learned one way in which soil chemistry controls contaminant transport, and how meaningful relationships can be extracted even when the data are highly variable.

Soil ‐ water partitioning and mass transfer kinetics of 2, 4, 6 trinitrotoluene in highly contaminated soil

Abstract The soil distribution coefficient for highly concentrated TNT soil is determined. Different amounts of TNT soil are shaked with 250 ml of distilled water, and the concentration of TNT in water is monitored throughout the experiment. At the conclusion of the experiment, the remainder of TNT in the soil is determined. The soil distribution coefficient is calculated by dividing the concentration of TNT in the soil by the concentration of TNT in the water phase. The average soil distribution coefficient is found to be 4.86 L/kg The mass transfer coefficient was determined under static conditions. This experiment involves a comprehensive study of distribution profiles for TNT in the soil and solvent phases. The experiment was conducted under a controlled temperature environment, for extensive periods of time. Samples were collected on a regular basis and were analyzed promptly. The average value of the mass transfer coefficient was determined to be 3.4×10‐3 hr‐1.

Iodine dispersion and effects on groundwater chemistry following a release to a peat bog, Manitoba, Canada

The migration and behaviour of I was investigated in a sphagnum bog on the Precambrian Shield in eastern Manitoba, Canada. A 6 M solution of KI was released at the base of the bog to stimulate a pulse discharge of contaminated groundwater from a fracture in the granitic rock. A network of piezometer tubes was used to monitor the dispersion of the I and the groundwater chemistry over 1 year. Cores of peat were also taken for analysis to supplement the groundwater data and to investigate the sorption of I. The introduced I dispersed 2 m horizontally and 1 m vertically within a month. After this, the system stabilized and further migration was insignificant. The pattern of I dispersion indicated that the bog hydrology was very complex with flow directions changing substantially with depth. The groundwater concentrations of the major cations rose in response to the mass action effect of K displacing them from reaction sites in the peat. Humic materials in the groundwater decreased in size after the KI release and returned to their pre-release conformation one month later. The geometric mean soil distribution coefficient value, K d , for I in the bog was 1.36l/kg, but it was strongly related to pore water concentration. Thus, a single K d value was insufficient for describing the system. Clearly, good groundwater chemistry, sorption and hydrological data are necessary for meaningful modelling of contaminant transport in bogs.